Generalizable Friction Coefficient Estimation via Material Embedding and Proxy Interaction Modeling

2026-04-27

AI Summary

This work addresses the scalability challenge in predicting friction coefficients for material pairs, where conventional approaches require exhaustive pairwise experiments whose cost grows quadratically with the number of materials. To overcome this limitation, the authors propose a proxy-interaction-based material embedding framework that learns representations of target materials through interactions with a small set of carefully selected proxy materials. A fusion function then predicts friction coefficients for arbitrary material pairs from their embeddings. The method integrates deterministic and probabilistic mappings, an optimized proxy selection strategy, and a robust mechanism for handling missing data, combining deep learning with physical priors. Evaluated on both simulated and real-world datasets, the approach achieves high prediction accuracy with substantially reduced experimental effort, maintains robustness under partial observability and noise, and enables interpretable embeddings alongside calibrated uncertainty quantification.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Accurately estimating friction coefficients between arbitrary material pairs is critical for robotics, digital fabrication, and physics-based simulation, but exhaustive pairwise testing scales quadratically with the number of materials. We introduce a proxy-based modeling framework that approximates any pairwise friction $f(A,B)$ from a small, fixed set of proxy materials $C=[c_1,\dots,c_k]$ by learning a per-material embedding $z_A = g(f(A,c1),\dots,f(A,ck))$ and a fusion function $p$ such that $f(A,B)\approx p\big(z_A,z_B\big)$. We present deterministic and probabilistic realizations of $g$ and $p$, procedures for selecting diverse proxy sets, and mechanisms for handling missing or noisy proxy measurements. The learned embeddings are compact, interpretable, and enable calibrated uncertainty estimates for downstream decision making. On simulated and measured friction datasets, our approach achieves high predictive accuracy, robust performance with partial observations, and substantial experimental savings by significantly reducing pairwise testing.

Problem

• friction coefficient estimation
• material pairs
• generalizable modeling
• proxy interaction
• experimental scalability

Innovation

• material embedding
• proxy interaction modeling
• friction coefficient estimation
• generalizable prediction
• uncertainty quantification